A multi-bin rarefying method for evaluating alpha diversities in TCR sequencing data

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• T cell receptors (TCRs) are crucial for adaptive immunity and antigen recognition.
• Sequencing T cell receptor diversity is vital for understanding immune system dynamics.
• Variable library sizes across samples complicate accurate alpha diversity estimation and comparison.

Purpose of the Study:

• To develop and validate an improved rarefying approach for T cell receptor sequencing data.
• To address the confounding effect of library size variation on alpha diversity metrics.
• To enhance the accuracy and reliability of immune repertoire analysis.

Main Methods:

• Developed a novel "multi-bin" rarefying approach.
• Partitioned samples into bins based on library size for within-bin rarefying.
• Conducted meta-analysis across bins to integrate results.
• Performed extensive simulations using real-world data.

Main Results:

• The overall rarefying approach was found to be inadequate for controlling library size confounding.
• The multi-bin rarefying method demonstrated robustness in addressing library size effects.
• The proposed method outperformed existing normalization strategies.
• Achieved better control of type-I error rates and enhanced statistical power in association tests.

Conclusions:

• The multi-bin rarefying approach provides a more accurate and reliable method for analyzing T cell receptor sequencing data.
• This method effectively mitigates the confounding effects of library size variation.
• The findings offer significant improvements for comparative immune repertoire studies.